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Synopsis
Filtering and system identification are powerful techniques for building models of complex systems. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques. Emphasis is on the least squares approach as applied to the linear state-space model, and problems of increasing complexity are analyzed and solved within this framework, starting with the Kalman filter and concluding with the estimation of a full model, noise statistics and state estimator directly from the data. Key background topics, including linear matrix algebra and linear system theory, are covered, followed by different estimation and identification methods in the state-space model. With end-of-chapter exercises, MATLAB simulations and numerous illustrations, this book will appeal to graduate students and researchers in electrical, mechanical and aerospace engineering. It is also useful for practitioners. Additional resources for this title, including solutions for instructors, are available online at www.cambridge.org/9780521875127.
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Book Description
This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models built using filtering and system identification techniques. Homework-problems, MATLAB simulations and numerous illustrations, make this ideal for graduate students, researchers and practitioners in engineering disciplines. Additional resources, including solutions, are available online at www.cambridge.org/9780521875127.
About the Author
Vincent Verdult was an assistant professor in systems and control at the Delft University of Technology in The Netherlands, from 2001 to 2005, where his research focused on system identification for nonlinear state-space systems. He is currently working in the field of information theory.
"About this title" may belong to another edition of this title.
- PublisherCambridge University Press
- Publication date2007
- ISBN 10 0521875129
- ISBN 13 9780521875127
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages422
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Filtering and System Identification : A Least Squares Approach
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Hardcover. Condition: new. Hardcover. Filtering and system identification are powerful techniques for building models of complex systems. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques. Emphasis is on the least squares approach as applied to the linear state-space model, and problems of increasing complexity are analyzed and solved within this framework, starting with the Kalman filter and concluding with the estimation of a full model, noise statistics and state estimator directly from the data. Key background topics, including linear matrix algebra and linear system theory, are covered, followed by different estimation and identification methods in the state-space model. With end-of-chapter exercises, MATLAB simulations and numerous illustrations, this book will appeal to graduate students and researchers in electrical, mechanical and aerospace engineering. It is also useful for practitioners. Additional resources for this title, including solutions for instructors, are available online at This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models of complex systems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780521875127
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Filtering and System Identification: A Least Squares Approach
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Filtering and System Identification
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models of complex systems.Über den AutorMichel Verhaegen is professor and co-director of the Delft Center for Systems and Contr. Seller Inventory # 446951733
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